FSL methods can be broadly categorized into metric-learning, optimization-based, hallucination, data-adaptation, and probabilistic approaches [1]}. Metric-learning approaches such as Prototypical Networks [2]}, Relation Networks [3]}, the Neural Statistician [4]} and Matching Networks [5]}, learn a feature extractor capable of parameterizing images into embeddings, and then use distance metrics to classify mapped query samples based on their distance to support points. Optimization-based approaches such as MAML [6]} and Meta-Learner LSTM [7]}), are meta-trained to use guided optimization steps on the support set for quick adaptation. Hallucination or data augmentation techniques perform affine and color transformations on the support set to create additional data points [8]}. Probabilistic methods use Bayesian inference to learn and classify samples, for example, the recently proposed Deep Kernel Transfer (DKT) [9]}, which uses Gaussian Process at inference time. We use the term domain adaptation to represent those approaches using standard transfer-learning with a pre-training stage on a large set of classes and a fine-tune stage on the support set – examples are Baseline and Baseline++ from [1]}. Additionally, the methods above can be paired with transductive inference mechanisms [11]}, [12]}, [13]}, [14]} which take advantage of the query set as an additional unlabeled set for task adaptation. However, we do not consider these works in our experiments since they assume access to the query set, limiting their practical application and giving them an unfair advantage. Although some mechanisms can use a single query sample at a time, which would alleviate these problems, we leave this consideration for future work. The details of the methods used in our experiments are reported in Appendix .
